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Conference

AUTOTESTCON 

About: AUTOTESTCON is an academic conference. The conference publishes majorly in the area(s): Automatic test equipment & Test Management Approach. Over the lifetime, 2171 publications have been published by the conference receiving 10392 citations.


Papers
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Book ChapterDOI
05 Nov 2007
TL;DR: This paper provides a high level architecture and includes descriptions of the OGC sensor interface and encoding standards that have been approved or are soon to be approved.
Abstract: This document provides a high level overview if the Sensor Web Enablement work of the Open Geospatial Consortium. This paper provides a high level architecture and includes descriptions of the OGC sensor interface and encoding standards that have been approved or are soon to be approved.

761 citations

Journal ArticleDOI
20 Sep 1994
TL;DR: This paper presents a comprehensive methodology for a formal, but intuitive, cause-effect dependency modeling using multi-signal directed graphs that correspond closely to hierarchical system schematics and develop diagnostic strategies to isolate faults without making the unrealistic single fault assumption.
Abstract: In this paper, we present a comprehensive methodology for a formal, but intuitive, cause-effect dependency modeling using multi-signal directed graphs that correspond closely to hierarchical system schematics and develop diagnostic strategies to isolate faults in the shortest possible time without making the unrealistic single fault assumption A key feature of our methodology is that our models lend naturally to real-world necessities, such as system integration and hierarchical troubleshooting >

214 citations

Proceedings ArticleDOI
20 Aug 2001
TL;DR: The prognostic and assessment methodology proposed here may be combined with diagnostic and maintenance scheduling methods and implemented on a conventional computing platform to serve the needs of industrial and other critical processes.
Abstract: Prognostic algorithms for condition based maintenance of critical machine components are presenting major challenges to software designers and control engineers. Predicting time-to-failure accurately and reliably is absolutely essential if such maintenance practices are to find their way into the industrial floor. Moreover, means are required to assess the performance and effectiveness of these algorithms. This paper introduces a prognostic framework based upon concepts from dynamic wavelet neural networks and virtual sensors and demonstrates its feasibility via a bearing failure example. Statistical methods to assess the performance of prognostic routines are suggested that are intended to assist the user in comparing candidate algorithms. The prognostic and assessment methodology proposed here may be combined with diagnostic and maintenance scheduling methods and implemented on a conventional computing platform to serve the needs of industrial and other critical processes.

181 citations

Proceedings ArticleDOI
05 Nov 2007
TL;DR: The RVM, which is a Bayesian treatment of the support vector machine (SVM), is used for diagnosis as well as for model development, and the PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL.
Abstract: The application of the Bayesian theory of managing uncertainty and complexity to regression and classification in the form of relevance vector machine (RVM), and to state estimation via particle filters (PF), proves to be a powerful tool to integrate the diagnosis and prognosis of battery health. Accurate estimates of the state-of-charge (SOC), the state-of-health (SOH) and state-of-life (SOL) for batteries provide a significant value addition to the management of any operation involving electrical systems. This is especially true for aerospace systems, where unanticipated battery performance may lead to catastrophic failures. Batteries, composed of multiple electrochemical cells, are complex systems whose internal state variables are either inaccessible to sensors or hard to measure under operational conditions. In addition, battery performance is strongly influenced by ambient environmental and load conditions. Consequently, inference and estimation techniques need to be applied on indirect measurements, anticipated operational conditions and historical data, for which a Bayesian statistical approach is suitable. Accurate models of electro-chemical processes in the form of equivalent electric circuit parameters need to be combined with statistical models of state transitions, aging processes and measurement fidelity, need to be combined in a formal framework to make the approach viable. The RVM, which is a Bayesian treatment of the support vector machine (SVM), is used for diagnosis as well as for model development. The PF framework uses this model and statistical estimates of the noise in the system and anticipated operational conditions to provide estimates of SOC, SOH and SOL. Validation of this approach on experimental data from Li-ion batteries is presented.

175 citations

Proceedings ArticleDOI
01 Sep 2006
TL;DR: Prognostics and health management (PHM) is an approach to system life-cycle support that seeks to reduce/eliminate inspections and time-based maintenance through accurate monitoring, incipient fault detection, and prediction of impending faults as discussed by the authors.
Abstract: Prognostics and health management (PHM) is an approach to system life-cycle support that seeks to reduce/eliminate inspections and time-based maintenance through accurate monitoring, incipient fault detection, and prediction of impending faults. Coupled with autonomic logistics for unprecedented responsiveness, cost effectiveness, and mission availability, PHM is largely automated in its application. Incorporating the principles of condition-based maintenance (CBM) along with the tenets of reliability-centered maintenance (RCM), the PHM paradigm extends these capabilities and provides a robust environment to optimize maintenance and logistics for increased operational availability (A0), and reduced life-cycle costs (LCC) while potentially increasing the reliability and life expectancy of mechanical, structural, and electronic systems. Driven by a demand for greater reliability at reduced cost and fueled by technological advancements, the PHM contribution to an already robust and confounding vocabulary surrounding maintenance and logistics is significant. As adopters of PHM technology attempt to define requirements and performance parameters, difficulties encountered with various non- standardized terminology indicate that the PHM vocabulary merits a lexical review. This paper will provide a compendium of PHM terminology along with definitions and examples, derived from the authors' experience in the implementation of PHM systems. Coalescing existing vocabularies and introducing, formally, the new lexicon of maintenance and logistics, the authors seek to aid in clarification of the emerging dialogue of life-cycle support.

123 citations

Performance
Metrics
No. of papers from the Conference in previous years
YearPapers
201939
201852
201745
201659
201565
201449